DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a report generator in claim 24.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 29-32 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 29-32 recite the limitation "The method of claim 24” in line 1. Claim 24 is directed to a system and does not disclose a method.
Claim limitation “report generator” in claim 24 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The claims and specification fail to disclose what the report generator is intended to be. It is unclear if it’s intended to be a printer, computer display, audio recording, etc. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 25-32 inherit the same deficiency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more. The claim(s) recite(s) a method for a mental process of providing neurostimulation therapy and predicting response of a patient thereto, which can be considered as a mental process and/or implemented with pen/paper. The steps of “obtaining data”, “analyzing the data”, “generating a report” and “determining whether the patient will respond to a neurostimulation treatment” could be interpreted as a provider receiving a patient’s medical report and reviewing the report to determine treatment appropriateness.
This judicial exception is not integrated into a practical application because the claims as written can be performed in a provider’s mind, or implemented with pen/paper and further do not positively recite any application of these steps in claims 1-23. While claim 1 recites the additional elements of “using one or more machine learning methods” these additional elements are not sufficient to amount to significantly more than the judicial exception because it is simply reciting using a machine learning method to analyze data, which could be implemented using a generic computer application lacking any further specification. Such a machine learning model for data processing and generation is well-understood and conventional in the art as suggested by (U.S. 20210361948).
Dependent claims 2-23 merely further limit generic steps, which can be performed mentally and/or by pen/paper.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-7, 9-13, 16, and 18-31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Maron-Katz (US20220296903A1).
Regarding claim 1, Maron-Katz discloses a method for providing neurostimulation therapy and predicting response of a patient thereto (Abstract), comprising: obtaining data from the patient having a neurological disorder or a psychiatric disorder (Paragraph [0013] "prediction of relapse of a neurological or a psychiatric disorder. The systems may include machine learning, and run algorithms that use patient data such as patient characteristics, treatment history, clinical history, biometric data, neuroimaging data, or a combination thereof, as inputs to generate a predictive model. The predictive model may predict patient relapse with minimal clinician intervention"); analyzing the data using one or more machine learning methods, wherein analyzing includes selecting one or more features from the data (Paragraph [0035] "The systems described herein may generally include a device configured to obtain one or more data features from the patient and a data module. The device for obtaining the one or more data features may comprise a computer, a laptop, a tablet computer, a mobile phone, a smart-watch, a ring configured to collect data features, or an implantable or partially-implanted device such as an implant dedicated to collection of data features or a neurostimulation implant additionally configured to collect data features. The data module may comprise one or more processors configured to run a machine learning algorithm, where the machine learning algorithm may be configured to analyze the one or more data features, generate a mood report based on the analyzed one or more data features, generate a mood plot having a mood threshold predetermined for the patient based on a plurality of mood reports taken over a plurality of neurostimulation treatment sessions, and predict relapse of the neurological or the psychological disorder in the patient based on the mood plot. Relapse may be predicted if the machine learning algorithm determines that the mood plot does not meet the predetermined mood threshold for the patient."); generating a report based on the selected one or more features (Paragraph [0035]); and determining whether the patient will respond to a neurostimulation treatment based on the report (Paragraph [0035]).
Regarding claim 2, Maron-Katz further discloses wherein the neurological disorder is Parkinson's disease, essential tremor, stroke, epilepsy, traumatic brain injury, migraine headache, cluster headache, or chronic pain (Paragraph [0018] "Exemplary neurological disorders in which the methods may be used to predict relapse include without limitation, Parkinson's disease, essential tremor, stroke, epilepsy, traumatic brain injury, migraine headache, cluster headache, or chronic pain.").
Regarding claim 3, Maron-Katz further discloses wherein the psychiatric disorder is depression, treatment-resistant depression, anxiety, post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD), a substance use disorder, bipolar disorder, or schizophrenia (Paragraph [0018] "The methods described herein may be used to predict relapse of various psychiatric disorders such as, but not limited to, depression, treatment-resistant depression, anxiety, post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD), a substance use disorder, bipolar disorder, or schizophrenia").
Regarding claim 4, Maron-Katz further discloses wherein the one or more machine learning methods comprises Linear Discriminant Analysis (LDA) (Paragraph [0036] "the machine learning algorithm may be, for example... linear discriminant analysis").
Regarding claim 6, Maron-Katz further discloses wherein obtaining data comprises acquiring data about a characteristic of the patient, a treatment history of the patient, a clinical history of the patient, biometric data, neuroimaging data, or a combination thereof (Paragraph [0013]).
Regarding claim 7, Maron-Katz further discloses wherein the one or more features are selected from a psychometric inventory (Paragraph [0074]-[0075] "The Montgomery-Asberg Depression Rating Scale (MADRS) is a widely used, ten-item diagnostic questionnaire that clinicians use to measure the severity of depressive episodes in patients. Administration of the MADRS is based on a clinical interview and assesses the following: apparent sadness, reported sadness, inner tension, reduced sleep, reduced appetite, concentration difficulties, lassitude, inability to feel, pessimistic thoughts, and suicidal thoughts... a predictive model may be used that provides an estimation of a clinician administered inventory such as the MADRS... MARDS-S to MADRS (410). Thereafter, the most recent MADRS and converted MADRS may be used as part of the MADRS Outcomes for Model Training...").
Regarding claim 9, Maron-Katz further discloses wherein the psychometric inventory is the Montgomery-Asberg Depression Rating Scale (MADRS) (Paragraph [0074]-[0075]).
Regarding claim 10, Maron-Katz further discloses wherein the one or more features selected from the psychometric inventory comprises apparent sadness, reported sadness, inner tension, reduced sleep, reduced appetite, concentration difficulty, lassitude, inability to feel, pessimistic thoughts, and suicidal thoughts (Paragraph [0074]-[0075]).
Regarding claim 11, Maron-Katz further discloses wherein the one or more features comprises reduced sleep (Paragraph [0074]-[0075]).
Regarding claim 12, Maron-Katz further discloses wherein the one or more features selected from the psychometric inventory comprises depressed mood, feelings of guilt, suicide, insomnia, effect on work, effect on activities, somatic anxiety, psychic anxiety, somatic gastro-intestinal symptoms, general somatic symptoms, genital symptoms, and weight loss (Paragraph [0037] "Examples of psychometric data include without limitation, information relating to mind wandering, anxiety, processing speed, task switching ability, attention, loneliness, or a combination thereof.").
Regarding claim 13, Maron-Katz further discloses wherein the one or more features are selected from a cognitive assessment (Paragraph [0070] "in addition to or instead of predicting relapse, the Decision Rule (208) predicts the score of a clinician administered inventory such as the MADRS, or predicts whether the score on a clinician administered inventory is above or below a given score, for example a threshold associated with relapse such as a score of 10. In step (214), the clinician may make a decision about re-treatment based on the score of the actual MADRS that is administered when the patient is brought back in, and/or clinician feedback on whether the patient is actually judged to need retreatment. This actual assessment score and/or clinical decision may be provided back to the Decision Rule (208) to help refine the algorithm over time").
Regarding claim 16, Maron-Katz further discloses wherein the machine learning method is Linear Discriminant Analysis (LDA) and the one or more features comprises reduced sleep, lassitude, retardation, and reduced appetite (Paragraph [0036]; Paragraphs [0074]-[0075]).
Regarding claim 18, Maron-Katz further discloses comprising selecting a course of treatment for the patient based on the report (Paragraphs [0015]-[0016] "The data module may comprise one or more processors configured to run a machine learning algorithm, where the machine learning algorithm may be configured to analyze the one or more data features, generate a mood report based on the analyzed one or more data features... the onset of relapse is predicted, the system may be configured to issue an alert or other warning signal that notifies the patient and/or the clinician of the relapse, and that treatment, for example, maintenance treatment, is needed. The alert may be an audible alarm, a visual alarm, a text, an email... ln some instances, the system may include a treatment device, for example, a transcranial magnetic stimulation (TMS) device, that may be automatically triggered or manually activated to deliver neurostimulation therapy upon receipt of the prediction of relapse.").
Regarding claim 19, Maron-Katz further discloses comprising delivering the neurostimulation treatment to the patient (Paragraphs [0015]-[0016]).
Regarding claim 20, Maron-Katz further discloses comprising sending an alert to a clinician when it is determined that the patient will not respond to the neurostimulation treatment (Paragraphs [0015]-[0016]).
Regarding claim 21, Maron-Katz further discloses wherein the alert is provided by email, text message, or an audible sound (Paragraphs [0015]-[0016]).
Regarding claim 22, Maron-Katz further discloses comprising sending an alert to the patient when it is determined that the patient will not respond to the neurostimulation treatment (Paragraphs [0015]-[0016]).
Regarding claim 23, Maron-Katz further discloses wherein the alert is provided by email, text message, or an audible sound (Paragraphs [0015]-[0016]).
Regarding claim 24, Maron-Katz discloses a system for providing neurostimulation therapy and predicting response of a patient thereto (Abstract), comprising: a device configured to obtain data from the patient having a neurological disorder or a psychiatric disorder (Paragraph [0013]); a data module comprising one or more processors configured to run one or more machine learning methods, wherein the one or more machine learning methods analyzes the data from the patient by selecting one or more features from the data (Paragraph [0035]); and a report generator configured to generate a report based on the selected one or more features (Paragraph [0035]).
Regarding claim 25, Maron-Katz further discloses wherein the device is a computer, a laptop, a tablet computer, a mobile phone, a smart watch, or a smart ring (Paragraph [0035]).
Regarding claim 26, Maron-Katz further discloses wherein the device is an implantable device or a partially implantable device (Paragraph [0035]).
Regarding claim 27, Maron-Katz further discloses comprising a treatment device (Paragraph [0015][ 0016]).
Regarding claim 28, Maron-Katz further discloses wherein the treatment device comprises a magnetic stimulation coil (Paragraph [0064] "An exemplary treatment device may be configured to deliver neurostimulation therapy. In one variation, the treatment device comprises a transcranial magnetic stimulation coil configured to deliver transcranial magnetic stimulation (TMS)").
Regarding claim 29, Maron-Katz further discloses wherein the neurological disorder is Parkinson's disease, essential tremor, stroke, epilepsy, traumatic brain injury, migraine headache, cluster headache, or chronic pain (Paragraph [0018]).
Regarding claim 30, Maron-Katz further discloses wherein the psychiatric disorder is depression, treatment-resistant depression, anxiety, post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD), a substance use disorder, bipolar disorder, or schizophrenia (Paragraph [0018]).
Regarding claim 31, Maron-Katz further discloses wherein the one or more machine learning methods comprises Linear Discriminant Analysis (LDA) (Paragraph [0036]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 5, 17, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Maron-Katz (US20220296903A1) as applied to claims 1 and 24 above, and further in view of Fogel (US20200335219A1).
Regarding claim 5, Maron-Katz does not disclose wherein the one or more machine learning methods comprises Lasso Regression. However, Fogel discloses providing personalized prognostic profiles (Abstract) wherein the one or more machine learning methods comprises Lasso Regression (Paragraph [0160] "the database relating to the population PO, that is used to generate a predictive model, is created for the occurrence of the outcome of interest over the first of the important time intervals (i.e., T1) specified by the user. This predictive model may be referred to as M1. The predictive model M1 may take any accepted form for the prediction of a binary outcome, including without limitation... a lasso regression..."; Paragraph [0220] "Each step in the process of selecting a matched population requires the construction and validation of a predictive model, based on a dataset of particular relevance to the patient of interest and his or her clinical situation. The actual model form can be... a lasso regression.... Software for predictive modeling can operate on subsets of the reference database to create and validate models of various types in real time, responsive to user selections of forced match variables and of time intervals and treatments of interest, user-required matches of risk factors, and the user's time points of interest"). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Maron-Katz, with a lasso regression as taught by Fogel, since such a modification would provide the predictable results of allowing users to create and validate models of various types in real time, responsive to user selections of forced match variables and of time intervals and treatments of interest, user-required matches of risk factors, and the user's time points of interest (Fogel, Paragraph [0220]).
Regarding claim 17, Maron-Katz further discloses the one or more features comprises reduced sleep and pessimistic thoughts (Paragraphs [0074]-[0075]), but does not disclose the machine learning method is Lasso Regression. However, Fogel discloses providing personalized prognostic profiles, wherein the machine learning method is Lasso Regression (Paragraphs [0160] and [0220]). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Maron-Katz, with a lasso regression as taught by Fogel, since such a modification would provide the predictable results of allowing users to create and validate models of various types in real time, responsive to user selections of forced match variables and of time intervals and treatments of interest, user-required matches of risk factors, and the user's time points of interest (Fogel, Paragraph [0220]).
Regarding claim 32, Maron-Katz does not disclose wherein the one or more machine learning methods comprises Lasso Regression. However, Fogel discloses providing personalized prognostic profiles (Abstract) wherein the one or more machine learning methods comprises Lasso Regression (Paragraphs [0160] and [0220]). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the system as taught by Maron-Katz, with a lasso regression as taught by Fogel, since such a modification would provide the predictable results of allowing users to create and validate models of various types in real time, responsive to user selections of forced match variables and of time intervals and treatments of interest, user-required matches of risk factors, and the user's time points of interest (Fogel, Paragraph [0220]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Maron-Katz (US20220296903A1) as applied to claim 7 above, and further in view of Fedor (US20190117143A1).
Regarding claim 8, Maron-Katz does not disclose wherein the psychometric inventory is the Hamilton Depression Rating Scale (HAM-D). However, Fedor a method and system for estimating a patient's level of depression throughout a monitoring period (Abstract), wherein the psychometric inventory is the Hamilton Depression Rating Scale (HAM-D) (Paragraph [0007] "the automated system estimates or predicts a user's level of depression. This estimate or prediction may be quantified. Likewise, this estimate or prediction may be expressed in terms of any depression rating scale. For instance, in some cases, the estimate or prediction is expressed in terms of the Hamilton Depression Rating Scale (HORS)."; Paragraph [0011] "During a training period, the automated system accepts, as input, depression ratings by a clinician (e.g., bi-weekly HORS ratings by clinicians) for multiple patients. During the training period, the automated system accepts, as input, self-reports by the patients (e.g., answers to surveys, multiple times daily)… The system creates an enlarged dataset of depression ratings for the training period, comprising the ratings by clinicians and the ratings estimated from the patient self-reports. A machine learning program is trained on a training dataset, which training dataset comprises: (1) the enlarged dataset of depression ratings for the training period; and (2) the physiological data, SMS usage data and smartphone usage data gathered during the training period. After the training (e.g., at the start of a monitoring period or later), the system accepts, as input, a depression rating by a clinician regarding a new patient. During the monitoring period, the system gathers passive data regarding the new patient, which passive data comprises the same type of physiological data, SMS usage data, and smartphone usage data. The system employs the trained ensemble model to estimate, based on this passive data and the clinician depression rating, one or more depression ratings for the new patient (e.g., a depression rating for each of multiple dates during the monitoring period other than the date of the clinician depression rating)"). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Maron-Katz, to include HORS ratings by clinicians as taught by Fedor, since such a modification would provide the predictable results of allowing the trained ensemble model to estimate, based on this passive data and the clinician depression rating, one or more depression ratings for the new patient (Fedor, Paragraph [0011]).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Maron-Katz (US20220296903A1) as applied to claims 1 and 13 above, and further in view of Restoration Healthcare ("Why We Use Cambridge Brain Sciences to Evaluate Cognitive Function", 2020).
Regarding claims 14 and 15, Maron-Katz does not disclose the cognitive assessment is the Creyos cognitive assessment including a token search. However, Restoration Healthcare discloses the Cambridge Brain Sciences (CBS) brain health assessment service measures core elements of cognitive function and is used as a standard of practice to asses scientifically accurate measures of cognition and that the assessment includes a token search (Examiner notes that the old name of Creyos was Cambridge Brain Sciences. Page 2, "To help us evaluate cognitive function, we use the Cambridge Brain Sciences (CBS) brain health assessment service, which measures core elements of cognitive function, including memory, attention, reasoning, and verbal abilities .... One or more tasks need to be completed for the assessment, with each divided into the categories of memory and reasoning .... Token Search"). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Maron-Katz, with Cambridge Brain Sciences (CBS) brain health assessment service to include a token search as taught by Restoration Healthcare, since such a modification would provide the predictable results of allowing a user to measure core elements of cognitive function, including memory, attention, reasoning, and verbal abilities (Restoration Healthcare, Page 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Vaughn (KR20220004639A) discloses a system and method for assessing a subject for a developmental state or conditions and providing improved digital therapy.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Marc D Honrath whose telephone number is (571)272-6219. The examiner can normally be reached M-F 7:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles A Marmor II can be reached at (571) 272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES A MARMOR II/Supervisory Patent Examiner
Art Unit 3791
/M.D.H./Examiner, Art Unit 3791
Prosecution Insights